ArticlePloS one2024
Performance of ChatGPT in medical licensing examinations in countries worldwide: A systematic review and meta-analysis protocol.
Article in PloS one, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
6 citing papers in PubMed.
- Rehabilitation Protocols Following Patellar Instability Surgery: A Structured Review of Published Recommendations and AI Generated Guidance.Current reviews in musculoskeletal medicine · 2026Review
- Validity and reliability of ChatGPT's responses on dietary supplements in Japan: A quality assessment and content analysis.PEC innovation · 2026Article
- Three large language models demonstrate competitive performance in Traditional Chinese Medicine national medical licensing examinations over two years.Scientific reports · 2026Article
- Textbook-level medical knowledge in large language models: comparative evaluation using Japanese National Medical Examination.BMC medical informatics and decision making · 2026Article
- The temporal changes in GPT-4 performance on UKMLA practice questions: educational and clinical implications.Frontiers in medicine · 2026Article
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Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionIn November 2022, the online artificial intelligence (AI) chatbot ChatGPT was released to the public, and swiftly garnered global attention because of its ability to provide detailed answers to complex queries. In medical field, ChatGPT has shown great potential to be used in medical education and has excelled in many English-language medical licensing examinations. However, due to the variability of medical licensing examinations in different countries, and ChatGPT's particular proficiency in English, the previous literatures showed that ChatGPT is unable to pass medical licensing examinations from non-English-speaking countries or those not administered in English. To the best of our knowledge, this is the first study to review whether ChatGPT can demonstrate consistent accuracy across diverse medical licensing examinations and be used in medical education across countries.
objectiveIn this study protocol, we aimed to analyze and review the differences in performance of ChatGPT in medical exams in various language environments and countries, as well as its potential in medical education. METHODS AND ANALYSIS: A systematic review and meta-analysis was conducted using PubMed, Web of Science, and Scopus to collect papers testing the performance of ChatGPT in medical licensing examinations. We imported all the collected literatures into Rayyan and screened the literatures based on the selection criteria and exclusion criteria. The risk of bias and quality of included studies was assessed by using Mixed Methods Appraisal Tool (MMAT). Data from included studies was extracted into an Excel spreadsheet. All of the above processes were completed by two reviewers independently. A third reviewer was consulted in cases of disagreement. Finally, we provided both quantitative and qualitative analysis of the findings from the included studies.
trial registrationPROSPERO registration number: CRD42024506687.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.